Shape the cloud foundation that thousands of engineers across the firm depend on. This is your opportunity to work at the base layer of one of the world's most complex cloud estates — solving problems that matter at scale, across AWS, Azure, and Google Cloud. You will join a team that has been building this foundation for over a decade, and you will help define what comes next.
We are growing substantially and hiring senior engineers into three areas: extending and launching a replacement provisioning platform on Azure while decommissioning what it replaces; engineering and supporting our AWS account provisioning platform, including the release lifecycle behind it; and designing, building, and operationalizing a platform provisioning capability from scratch. You do not need depth in all three areas or across every cloud — tell us where your expertise lies and we will find the right fit.
As a Lead Software Engineer at JPMorganChase within Infrastructure Platforms Cloud Foundation Services, you are an integral part of an agile team that works to enhance, build, and deliver trusted, market-leading technology products in a secure, stable, and scalable way. You will apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and cloud platforms. These are software engineering roles focused on platform development, and there is genuine scope to build breadth across more than one cloud as the team grows.
The platform provisioning work is multi-cloud by requirement rather than aspiration: the first delivery targets AWS, but the architecture must extend to Azure and Google Cloud without fundamental redesign — meaning the earliest design decisions must hold across all three providers.
Job responsibilities
- Develop secure, high-quality production code for cloud platform services and tooling, and review and debug code written by others
- Own significant technical design decisions, contributing to product design, platform architecture, and the technical processes that keep the platform reliable
- Build deep knowledge of the platform and share it deliberately, ensuring critical capabilities are never held by a single engineer
- Mentor other engineers and help raise the engineering practices of the wider team
- Contribute to the engineering community as an advocate of firmwide frameworks, tools, and practices across the Software Development Life Cycle
- Influence peers and project decision-makers to consider the use and application of leading-edge technologies
- Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation
- Foster a team culture of diversity, opportunity, inclusion, and respect
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and advanced applied experience
- Advanced proficiency in one or more programming languages such as Java, Python, or Go
- Hands-on experience with at least one major public cloud — AWS, Azure, or Google Cloud
- Hands-on experience with Terraform for infrastructure as code, applied from a software engineering perspective
- Experience designing, developing, and maintaining production software systems consumed by other engineering teams
- Experience owning technical design decisions and mentoring other engineers
- Knowledge of cloud-native architecture, distributed systems, and microservices design patterns, including scalability, reliability, and fault tolerance
- Proficiency across the Software Development Life Cycle, including design, development, testing, and deployment
- Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security
- Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices
Preferred qualifications, capabilities, and skills
- Experience engineering and supporting high-volume production platforms, including release engineering, continuous delivery, and automated testing strategy
- Background in systems design and systems engineering at platform level, ideally including the full operational lifecycle — standing a platform up, running it, and recovering it
- Depth in cloud identity, networking, and policy controls at enterprise scale
- Breadth across more than one of AWS, Azure, and Google Cloud, or a track record of building platform abstractions that hold across providers
- Experience delivering against regulatory or supervisory requirements in a regulated industry such as financial services
- Comfort working in an environment where pairing and software teaming are common practices to foster collaboration and knowledge sharing